Robust Predictions for DSGE Models with Incomplete Information
提出一种方法，使不完全信息的DSGE模型预测在不同信息结构下保持稳健，并量化信息作为商业周期波动来源的重要性，发现企业特定需求冲击对信心波动起关键作用。
We provide predictions for DSGE models with incomplete information that are robust across information structures. Our approach maps an incomplete-information model into a full-information economy with time-varying expectation wedges and provides conditions that ensure the wedges are rationalizable by some information structure. Using our approach, we quantify the potential importance of information as a source of business cycle fluctuations in an otherwise frictionless model. Our approach uncovers a central role for firm-specific demand shocks in supporting aggregate confidence fluctuations. Only if firms face unobserved local demand shocks can confidence fluctuations account for a significant portion of the US business cycle.